A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV

Xia, Shaoxuan; Zhou, Xiaofeng; Shi, Haibo; Ли, Шуай; Xu, Chunhui

doi:10.1016/j.oceaneng.2022.112595

Cited by 15 publications

(4 citation statements)

References 31 publications

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“…Articles Attention (VI-D1) [56], [58], [64], [78], [133] Fuzzy (VI-B1) [52], [53], [126], [141] Knowledge-based (VI-B2) [103], [121], [142] Sparse Networks (VI-A1) [46], [104], [112] Interpretable Filters (VI-B3) [45], [49], [ [145] Rule-based Interpretations (VI-B8) [146] fault trees for domestic heaters. C4.5 is used to learn the failure thresholds of the sensor data.…”

Section: Methodsmentioning

confidence: 99%

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Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Cummins,

Sommers,

Ramezani

et al. 2024

IEEE Access

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Predictive maintenance is a well studied collection of techniques that aims to prolong the life of a mechanical system by using artificial intelligence and machine learning to predict the optimal time to perform maintenance. The methods allow maintainers of systems and hardware to reduce financial and time costs of upkeep. As these methods are adopted for more serious and potentially life-threatening applications, the human operators need trust the predictive system. This attracts the field of Explainable AI (XAI) to introduce explainability and interpretability into the predictive system. XAI brings methods to the field of predictive maintenance that can amplify trust in the users while maintaining well-performing systems. This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. We categorize the different XPM methods into groups that follow the XAI literature. Additionally, we include current challenges and a discussion on future research directions in XPM.

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Section: Methodsmentioning

confidence: 99%

“…Xia et al[58] and Hafeez et al[64] tackled interpretable fault diagnosis in two separate ways. Xia et al looked at hierarchical attention by grouping the features by systems and subsystems.…”

mentioning

confidence: 99%

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Cummins,

Sommers,

Ramezani

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…[184][185][186][187][188][189] use the semantic web rule language, rule-based expert systems, fuzzy systems, quantitative association rule mining (QARM), and data-driven sensitivity analysis. In [190][191][192][193][194][195][196][197][198][199][200][201][202][203][204], some techniques and visualizations such as decision trees, graph-based approaches, the attention modules used together with LSTM, generative adversarial networks (GAN), PDPs, and feature importance calculation methods. Refs.…”

Section: Transparency and Explainability In Ai-based Predictive Maint...mentioning

confidence: 99%

“…The Approach Application [197] The attention mechanism RUL estimation on the NASA turbofan engine dataset [132,133] [198] The attention mechanism Structural Health Monitoring [199] The multi-layer, multi-source attention distribution…”

Section: Refmentioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.

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Squeeze‐and‐excitation attention residual learning of propulsion fault features for diagnosing autonomous underwater vehicles

Du,

Yu,

Guo

et al. 2024

Journal of Field Robotics

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Given the demanding and unpredictable operational conditions, autonomous underwater vehicles (AUVs) often encounter different propulsion faults, leading to significant economic losses and mission impairments. To address this challenge, vibratory time‐series features can be extracted for the precise propulsion fault diagnosis of AUVs. A squeeze‐and‐excitation (SE) attention residual network (SEResNet) is therefore put forward to enhance the feature extraction for AUV propulsion fault diagnosis. By leveraging the vibratory time‐series data obtained from the AUV, an SE attention mechanism is embedded into a residual network. This integration facilitates the extraction of pertinent vibratory fault features, subsequently utilized for accurate diagnosis of any propulsion faults. The effectiveness of the proposed SEResNet was validated through its application to an actual experimental AUV, with comparison against the state‐of‐the‐arts. The results reveal that the present SEResNet outperforms all other comparison methods in terms of diagnosis performance for AUV propulsion faults.

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A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV

Cited by 15 publications

References 31 publications

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Squeeze‐and‐excitation attention residual learning of propulsion fault features for diagnosing autonomous underwater vehicles

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